ABOUT THE ROLEBaker Botts is building its Enterprise Data Services team and is seeking an Analytics Engineer to help transform enterprise data into trusted, analytics-ready assets that support reporting, business intelligence, and AI initiatives across the firm.
Working primarily within Azure Databricks and Entegrata, this role focuses on designing and maintaining data transformations, implementing business logic, ensuring data quality, and creating curated datasets that enable meaningful insights and decision-making.
While other technical teams are responsible for delivering source data into the Bronze layer, this role owns the transformation of that data into business-ready datasets, responsible for developing and maintaining trusted Silver and Gold layer assets that support reporting, self-service analytics, operational intelligence, and future AI capabilities.
This role is ideal for someone who enjoys combining technical engineering skills with analytical thinking and stakeholder engagement. Success requires intellectual curiosity and the drive to improve end users' data and analytics experiences, strong Python and Databricks expertise, a passion for data quality, and the ability to translate business needs into scalable data solutions.
WHAT YOU'LL DOPrimary Responsibilities- Partner with business stakeholders and the EAI team to understand enterprise reporting and analytical requirements, translating them into scalable technical and data solutions.
- Define, document, and implement business rules, transformation logic, calculations, enrichment processes, data lineage, and data quality checks required to transform raw data into business-ready information aligned to business stakeholder needs and support governance efforts.
- Use Bronze-layer data to develop pipelines and transformations that create trusted Silver and Gold datasets for reporting, analytics, and AI use cases.
- Maintain curated datasets, reusable metrics, and analytical data products that support firmwide reporting and self-service analytics.
- Build, maintain, and optimize PySpark-based workflows, Delta Lake assets, and Databricks jobs.
- Perform data profiling, reconciliation, validation, and testing to ensure confidence in downstream reporting and decision-making.
- Support the development of dashboards, reports, and analytical solutions by providing clean, trusted, and well-documented datasets.
- Work with source system owners and technical teams to resolve data quality, completeness, and usability issues identified during transformation and analysis activities.
- Participate in code reviews, CI/CD processes, and understand established development standards alongside our partner Enterprise Applications & Integrations team.
- Identify opportunities to leverage advanced analytics, statistical methods, and AI-enabled solutions to improve business outcomes.
WHAT YOU'LL BRINGRequired- 5-7 years of experience in analytics engineering, data engineering, data analytics, business intelligence, or related field.
- Strong Python skills, including experience building data transformation, automation, and validation processes.
- Hands-on experience with Azure Databricks, including notebooks, workflows, Unity Catalog, Delta Lake, job orchestration, and performance optimization.
- Experience using PySpark to prepare, transform, and optimize large-scale datasets.
- Experience developing semantic models, analytical data marts, or subject area datasets.
- Experience designing, building, and maintaining analytics ready datasets and business facing data models.
- Experience with Power BI, Tableau, or other business intelligence and visualization platforms.
- Strong understanding of data quality, validation, testing, and governance practices.
- Experience working with cloud-based data platforms and modern lakehouse architectures.
- Ability to translate business requirements into scalable data solutions and meaningful analytical outputs.
- Strong written and verbal communication skills, including the ability to explain technical concepts to both technical and non-technical audiences.
- Proven ability to work independently while collaborating effectively across technical and business teams.
Preferred- Experience with Azure Data Lake Storage (ADLS Gen2).
- Familiarity with medallion architecture and modern lakehouse design patterns.
- Experience with machine learning, predictive analytics, or AI-enabled solutions.
- Exposure to dimensional modeling and analytical data modeling techniques.
- Basic SQL proficiency for data validation, troubleshooting, and exploratory analysis.
- Databricks, Azure, analytics, or business intelligence certifications.
SUCCESS IN THIS ROLESuccess in this role means delivering trusted, scalable data products that power reporting, analytics, and AI initiatives, while ensuring data quality, governance, and reliability. The successful candidate will be a valued partner to the business, translating complex questions into actionable data solutions and building reusable assets that advance the firm's long-term data and AI strategy.
Anticipated role breakdown by activity type:
- 60% Analytics Engineering (Bronze/Silver/Gold ownership, business logic, curated datasets, data products, data quality)
- 30% Data Engineering (Azure Databricks, PySpark, Delta Lake, pipeline development)
- 10% Analytics & AI Enablement (Reporting support, advanced analytics, AI use cases)
HOW YOU'LL WORKPhysical Requirements- Must be able to sit for extensive periods of time, either while using the telephone or computer.
- Must be able to work in a high-pressure environment with time restraints and frequent interruptions.
Working Conditions and Environment- Work is normally performed in a standard office environment but may, on occasion, involve moderate exposure to dust, dirt and/or extreme temperature.
- Position is full-time and requires a five-day workweek and standard hours as outlined in the firm policy manual. Must be available to work overtime, including weekend hours, when necessary to meet established deadlines or stay current with occasional peaks in workload. Must be willing to change regular work schedule to meet the needs of the Firm
- This role may be hybrid and will require a minimum of three days per week (or at least 60%) present in the office, and up to two days per week (40%) may be performed remotely
